A Data Science Perspective

Kickstarter. Most of us have seen Kickstarter projects be that in social media ads, blogposts or search results. Hell, you can even be one of those who treat Kickstarter like Instagram and browse it daily.

For the small portion of readers who don’t know what Kickstarter is, Kickstarter is a platform for launching your own products or services through crowdfunding. You can create a page with information about your creative product which can be anything from a tech product to a art masterpiece. You then set a funding goal. …

A RESNET approach to Convolutional Neural Networks in PyTorch

Humans produce millions of tons of garbage everyday. This garbage, as we know, needs to be segregated before it is taken out of our houses. As essential as this process is for the functioning of communities and sustaining of the mother earth, it is Tedious!

We all have, at some point imagined of having a robot personal assistant who will do all the chores for us, including garbage segregation. I mean, whom are we kidding? We hate to spend minutes staring at 6 garbage bins with a soda can in our hand and wondering where it goes. …

Torch your Data Science world alight with these Tensor related functions

PyTorch proficiency is one of the most sought after skill when it comes to recruitment for data scientists. For those who don’t know, PyTorch is a Python library with a wide variety of functions and operations, mostly used for deep learning. One of the most basic yet important parts of PyTorch is the ability to create Tensors. A tensor is a number, vector, matrix, or any n-dimensional array.

Now the question might be, ‘why not use numpy arrays instead?’

For Deep Learning, we would need to compute the derivative of elements of the data. PyTorch provides the ability to compute…

Learn exactly what they do, along with working and breaking examples

NumPy, as the name suggest, is a powerful and open source python library that helps us compute operations on primarily numbers, faster. It is an important tool for data science. NumPy lets us create multi dimensional arrays and lets us perform simple as well as complex operations like indexing, broadcasting, slicing, matrix multiplication to name a few. Today we’ll see how to organize your NumPy arrays better and compute some interesting operations using following functions. The functions are :

  • numpy.sort
  • numpy.count_nonzero
  • numpy.where
  • numpy.compress
  • numpy.trace

Aditya Patkar

A Culinary Arts graduate intrigued by all kinds of data. Self learning Data Science and Machine Learning.

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